Federated Linear Contextual Bandits

• URL: http://arxiv.org/abs/2110.14177v1
• Date: Wed, 27 Oct 2021 05:18:58 GMT
• Title: Federated Linear Contextual Bandits
• Authors: Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen
• Abstract summary: Fed-PE is proposed to cope with the heterogeneity across clients without exchanging local feature vectors or raw data. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.
• Score: 17.438169791449834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a novel federated linear contextual bandits model, where individual clients face different $K$-armed stochastic bandits coupled through common global parameters. By leveraging the geometric structure of the linear rewards, a collaborative algorithm called Fed-PE is proposed to cope with the heterogeneity across clients without exchanging local feature vectors or raw data. Fed-PE relies on a novel multi-client G-optimal design, and achieves near-optimal regrets for both disjoint and shared parameter cases with logarithmic communication costs. In addition, a new concept called collinearly-dependent policies is introduced, based on which a tight minimax regret lower bound for the disjoint parameter case is derived. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.

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